full transcript

## Unscramble the Blue Letters

Have you ever sat in a doctor's office for hours despite having an appointment at a specific time, has the hotel turned down your reservation because it's full? Or have you been bumped off a flight that you paid for? These are all symptoms of overbooking, a ptrcciae where bueissness and iioinsnttuts sell or book more than their full capacity. While often infuriating for the customer, overbooking happens because it increases profits while also letting businesses optizmie their resources. They know that not everyone will show up to their appointments, reservations and ftilhgs, so they make more available than they actually have to offer. Airlines are the classical example, partially because it happens so often, about 50000 poelpe get bumped off their flights each year. That figure comes at little surprise to the airlines themselves, which used ssatiittcs to determine exactly how many tickets to sell. It's a delicate operation, sell too few and they're wasting seats, sell too many and they pay penltieas, money, free flights, hotel stays and ayonned customers. So here's a sepilmfiid viosern of how their clatcolnaius work. Airlines have collected years worth of information about who does and doesn't show up for certain flights. They know, for example, that on a particular route, the probability that each individual customer will show up on time is 90 percent. For the sake of simplicity, will assume that every customer is tnlavierg individually rather than as families or groups, then if there are 180 seats on the plane and they sell 180 tickets, the most likely result is that 162 passengers will borad. But of course, you could also end up with more passengers or fewer. The prbblitaioy for each value is given by what's called a binomial distribution, which peaks at the most likely ocoutme. Now let's look at the revenue. The airline makes money from each ticket buyer and loses money for each person who gets bumped. Let's say a ticket costs 250 dollars and isn't exchangeable for a later flight and the cost of bumping a passenger is 800 dollars. These numbers are just for the sake of example. Actual amounts vary considerably. So here, if you don't sell any extra tickets, you make 45000 dollars. If you sell 15 extras and at least 15 people are no shows, you make forty eight tnaoushd seven hundred fifty dollars. That's the best case. In the wosrt case, everyone shows up, 15 unlucky passengers get bumped and the revenue will only be thirty six thousand seven hundred fifty dollars, even less than if you only sold 180 tickets in the first plcae. But what matters isn't just how good or bad a scenario is financially, but how likely it is to happen. So how likely is each scenario? We can find out by using the binomial distribution in this example, the probability of exactly 195 passengers boarding is almost zero percent. The probability of exactly 184 passengers boarding is one point one one percent and so on. Multiply these probabilities by the revenue for each case, add them all up and subtract the sum from the earnings by 195 sold tickets and you get the expected rvuenee for selling 195 tkteics. By repeating this calculation for various numbers of extra tickets, the airline can find the one likely to yield the highest revenue in this example. That's 198 tickets from which the aiinlre will probably make forty eight thousand seven hundred setenvy four dallors, almost 4000 more than without okoirevnbog. And that's just for one flight. Multiply that by a moliiln flights per airline per year. And overbooking adds up fast. Of course, the actual calculation is much more complicated airlines apply many factors to create even more accurate models, but should they? Some agrue that overbooking is unethical. You're charging two people for the same resource. Of course, if you're 100 percent sure someone won't show up, it's fine to sell their seat. But what if you're only 95 percent sure, 75 pcrenet. Is there a number that separates being uiaenchtl from being pacicrtal?

## Open Cloze

Have you ever sat in a doctor's office for hours despite having an appointment at a specific time, has the hotel turned down your reservation because it's full? Or have you been bumped off a flight that you paid for? These are all symptoms of overbooking, a ________ where __________ and ____________ sell or book more than their full capacity. While often infuriating for the customer, overbooking happens because it increases profits while also letting businesses ________ their resources. They know that not everyone will show up to their appointments, reservations and _______, so they make more available than they actually have to offer. Airlines are the classical example, partially because it happens so often, about 50000 ______ get bumped off their flights each year. That figure comes at little surprise to the airlines themselves, which used __________ to determine exactly how many tickets to sell. It's a delicate operation, sell too few and they're wasting seats, sell too many and they pay _________, money, free flights, hotel stays and _______ customers. So here's a __________ _______ of how their ____________ work. Airlines have collected years worth of information about who does and doesn't show up for certain flights. They know, for example, that on a particular route, the probability that each individual customer will show up on time is 90 percent. For the sake of simplicity, will assume that every customer is _________ individually rather than as families or groups, then if there are 180 seats on the plane and they sell 180 tickets, the most likely result is that 162 passengers will _____. But of course, you could also end up with more passengers or fewer. The ___________ for each value is given by what's called a binomial distribution, which peaks at the most likely _______. Now let's look at the revenue. The airline makes money from each ticket buyer and loses money for each person who gets bumped. Let's say a ticket costs 250 dollars and isn't exchangeable for a later flight and the cost of bumping a passenger is 800 dollars. These numbers are just for the sake of example. Actual amounts vary considerably. So here, if you don't sell any extra tickets, you make 45000 dollars. If you sell 15 extras and at least 15 people are no shows, you make forty eight ________ seven hundred fifty dollars. That's the best case. In the _____ case, everyone shows up, 15 unlucky passengers get bumped and the revenue will only be thirty six thousand seven hundred fifty dollars, even less than if you only sold 180 tickets in the first _____. But what matters isn't just how good or bad a scenario is financially, but how likely it is to happen. So how likely is each scenario? We can find out by using the binomial distribution in this example, the probability of exactly 195 passengers boarding is almost zero percent. The probability of exactly 184 passengers boarding is one point one one percent and so on. Multiply these probabilities by the revenue for each case, add them all up and subtract the sum from the earnings by 195 sold tickets and you get the expected _______ for selling 195 _______. By repeating this calculation for various numbers of extra tickets, the airline can find the one likely to yield the highest revenue in this example. That's 198 tickets from which the _______ will probably make forty eight thousand seven hundred _______ four _______, almost 4000 more than without ___________. And that's just for one flight. Multiply that by a _______ flights per airline per year. And overbooking adds up fast. Of course, the actual calculation is much more complicated airlines apply many factors to create even more accurate models, but should they? Some _____ that overbooking is unethical. You're charging two people for the same resource. Of course, if you're 100 percent sure someone won't show up, it's fine to sell their seat. But what if you're only 95 percent sure, 75 _______. Is there a number that separates being _________ from being _________?

## Solution

1. overbooking
2. outcome
3. revenue
4. statistics
5. dollars
6. percent
7. calculations
8. worst
10. institutions
11. unethical
12. place
13. simplified
14. seventy
15. board
16. argue
17. annoyed
18. practical
19. million
20. traveling
21. penalties
22. flights
23. people
24. optimize
25. airline
26. tickets
27. version
28. thousand
29. probability
30. practice

## Original Text

Have you ever sat in a doctor's office for hours despite having an appointment at a specific time, has the hotel turned down your reservation because it's full? Or have you been bumped off a flight that you paid for? These are all symptoms of overbooking, a practice where businesses and institutions sell or book more than their full capacity. While often infuriating for the customer, overbooking happens because it increases profits while also letting businesses optimize their resources. They know that not everyone will show up to their appointments, reservations and flights, so they make more available than they actually have to offer. Airlines are the classical example, partially because it happens so often, about 50000 people get bumped off their flights each year. That figure comes at little surprise to the airlines themselves, which used statistics to determine exactly how many tickets to sell. It's a delicate operation, sell too few and they're wasting seats, sell too many and they pay penalties, money, free flights, hotel stays and annoyed customers. So here's a simplified version of how their calculations work. Airlines have collected years worth of information about who does and doesn't show up for certain flights. They know, for example, that on a particular route, the probability that each individual customer will show up on time is 90 percent. For the sake of simplicity, will assume that every customer is traveling individually rather than as families or groups, then if there are 180 seats on the plane and they sell 180 tickets, the most likely result is that 162 passengers will board. But of course, you could also end up with more passengers or fewer. The probability for each value is given by what's called a binomial distribution, which peaks at the most likely outcome. Now let's look at the revenue. The airline makes money from each ticket buyer and loses money for each person who gets bumped. Let's say a ticket costs 250 dollars and isn't exchangeable for a later flight and the cost of bumping a passenger is 800 dollars. These numbers are just for the sake of example. Actual amounts vary considerably. So here, if you don't sell any extra tickets, you make 45000 dollars. If you sell 15 extras and at least 15 people are no shows, you make forty eight thousand seven hundred fifty dollars. That's the best case. In the worst case, everyone shows up, 15 unlucky passengers get bumped and the revenue will only be thirty six thousand seven hundred fifty dollars, even less than if you only sold 180 tickets in the first place. But what matters isn't just how good or bad a scenario is financially, but how likely it is to happen. So how likely is each scenario? We can find out by using the binomial distribution in this example, the probability of exactly 195 passengers boarding is almost zero percent. The probability of exactly 184 passengers boarding is one point one one percent and so on. Multiply these probabilities by the revenue for each case, add them all up and subtract the sum from the earnings by 195 sold tickets and you get the expected revenue for selling 195 tickets. By repeating this calculation for various numbers of extra tickets, the airline can find the one likely to yield the highest revenue in this example. That's 198 tickets from which the airline will probably make forty eight thousand seven hundred seventy four dollars, almost 4000 more than without overbooking. And that's just for one flight. Multiply that by a million flights per airline per year. And overbooking adds up fast. Of course, the actual calculation is much more complicated airlines apply many factors to create even more accurate models, but should they? Some argue that overbooking is unethical. You're charging two people for the same resource. Of course, if you're 100 percent sure someone won't show up, it's fine to sell their seat. But what if you're only 95 percent sure, 75 percent. Is there a number that separates being unethical from being practical?

## Frequently Occurring Word Combinations

### ngrams of length 2

collocation frequency
passengers boarding 2

## Important Words

1. accurate
2. actual
5. airline
6. airlines
7. amounts
8. annoyed
9. apply
10. appointment
11. appointments
12. argue
13. assume
15. binomial
16. board
17. boarding
18. book
19. bumped
20. bumping
23. calculation
24. calculations
25. called
26. capacity
27. case
28. charging
29. classical
30. collected
31. complicated
32. considerably
33. cost
34. costs
35. create
36. customer
37. customers
38. delicate
39. determine
40. distribution
41. dollars
42. earnings
43. exchangeable
44. expected
45. extra
46. extras
47. factors
48. families
49. fast
50. fifty
51. figure
52. financially
53. find
54. fine
55. flight
56. flights
57. forty
58. free
59. full
60. good
61. groups
62. happen
63. highest
64. hotel
65. hours
66. increases
67. individual
68. individually
69. information
70. infuriating
71. institutions
72. letting
73. loses
74. matters
75. million
76. models
77. money
78. multiply
79. number
80. numbers
81. offer
82. office
83. operation
84. optimize
85. outcome
86. overbooking
87. paid
88. partially
89. passenger
90. passengers
91. pay
92. peaks
93. penalties
94. people
95. percent
96. person
97. place
98. plane
99. point
100. practical
101. practice
102. probabilities
103. probability
104. profits
105. repeating
106. reservation
107. reservations
108. resource
109. resources
110. result
111. revenue
112. route
113. sake
114. sat
115. scenario
116. seat
117. seats
118. sell
119. selling
120. separates
121. seventy
122. show
123. shows
124. simplicity
125. simplified
126. sold
127. specific
128. statistics
129. stays
130. subtract
131. sum
132. surprise
133. symptoms
134. thousand
135. ticket
136. tickets
137. time
138. traveling
139. turned
140. unethical
141. unlucky
142. vary
143. version
144. wasting
145. work
146. worst
147. worth
148. year
149. years
150. yield